Deb, K, 1998, Non-linear Goal Programming Using Multi-Objective Genetic Algorithms, Technical Report No. CI-60/98, Department of Computer Science/XI, University of Dortmund, Germany.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....the aforementioned approximation techniques with stochastic algorithms, such as genetic or evolutionary. Evolutionary Multi objective Optimization (EMO) on the other hand, is a well established computational research area, whereby several powerful methods are available for POF approximation ( 5] [6]) The relevance of the possible link between EMO methods and GRS strategies is evident, thinking of the high computational costs of Multiobjective Evolutionary Algorithms (MOEAs) which may become impractical when industrial design is concerned [7] 2 Pareto Multi objective Optimization ....

K. Deb, "Nonlinear goal programming using multi-objective genetic algorithms," PJournal of the Operational Research Society, vol. 52, no. 3, pp. 291--302, 2001.

....2, where cw 1 is the center of weight of the first front and upoint the utopia point. In order to set up the sharing procedure we then evaluate the normalized average distances d i,j among elements, in both design and objectives domain. Afterwards we implement the standard sharing formulas [3] [4] for the calculation of the sharing parameter sh i,j and the penalty coefficient m i, we evaluate the niche radius s in the following way: ndof p p p x min x max x ) nset ( 1 2 1 1 s ( objf p q q f min f max f ) nset ( 1 2 1 1 s (2) considering s x ....

Deb, K.: Non-linear Goal Programming Using Multi-Objective Genetic Algorithm. Technical Report no. CI-60/98, Department of Computer Science, XI University of Dortmund GE, 1998

....convergence. We applied special genetic operators for decision variables as described by Michalewicz [13] Some of them offer the possibility for a better performance of the genetic algorithms in the late stage of the optimization process. For NPGA we use a sharing mechanism described by S. Deb [14]. The sharing parameter s share is given by: P share q s (7) where q is the desired number of distinct Pareto optimal solutions and P is the number of variables in the problem. Selecting the Solution from the Pareto Set After the last generation is processed by the SPEA, NRGA or NPGA ....

) Deb, K.: Non-linear goal programming using Multi-objective Genetic Algorithms. Technical Report CI-60/98, Department of Computer Science /LS11. University of Dortmund, Germany. (1999)

....2, where cw 1 is the center of weight of the first front and upoint the utopia point. In order to set up the sharing procedure we then evaluate the normalized average distances d i,j among elements in both design and objectives domain. Afterwards we implement the standard sharing formulas [3] [4]; however, we evaluate the niche radius s in the following way: ndof p p p x min x max x ) nset ( 1 2 1 1 s ( objf p q q f min f max f ) nset ( 1 2 1 1 s (2) considering s x or s f when shape or performances diversity has to be enhanced, ....

Deb, K.: Non-linear Goal Programming Using Multi-Objective Genetic Algorithm. Technical Report no. CI-60/98, Department of Computer Science, XI University of Dortmund GE, 1998

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Deb, K, 1998, Non-linear Goal Programming Using Multi-Objective Genetic Algorithms, Technical Report No. CI-60/98, Department of Computer Science/XI, University of Dortmund, Germany.

No context found.

K. Deb, Nonlinear goal programming using multi-objective genetic algorithms, Journal of the Operational Research Society, Vol. 52 (3), pp. 291-302 (2001).

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